SOTAVerified

Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 761–770 of 2226 papers

TitleStatusHype
Hamiltonian Adaptive Importance Sampling—0
Ensemble-based gradient inference for particle methods in optimization and samplingCode0
Batch Bayesian optimisation via density-ratio estimation with guaranteesCode0
Convolutional Bayesian Kernel Inference for 3D Semantic MappingCode1
Improved Marginal Unbiased Score Expansion (MUSE) via Implicit DifferentiationCode0
Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference—0
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian InferenceCode1
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Uncovering Regions of Maximum Dissimilarity on Random Process Data—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified